A Two-Stage SAR Image Generation Algorithm Based on GAN with Reinforced Constraint Filtering and Compensation Techniques

Ming Liu, Hongchen Wang, Shichao Chen, Mingliang Tao, Jingbiao Wei

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Generative adversarial network (GAN) can generate diverse and high-resolution images for data augmentation. However, when GAN is applied to the synthetic aperture radar (SAR) dataset, the generated categories are not of the same quality. The unrealistic category will affect the performance of the subsequent automatic target recognition (ATR). To overcome the problem, we propose a reinforced constraint filtering with compensation afterwards GAN (RCFCA-GAN) algorithm to generate SAR images. The proposed algorithm includes two stages. We focus on improving the quality of easily generated categories in Stage 1. Then, we record the categories that are hard to generate and compensate by using traditional augmentation methods in Stage 2. Thus, the overall quality of the generated images is improved. We conduct experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) acquired by the proposed algorithm indicate its effectiveness.

Original languageEnglish
Article number1963
JournalRemote Sensing
Volume16
Issue number11
DOIs
StatePublished - Jun 2024

Keywords

  • automatic target recognition (ATR)
  • generative adversarial network (GAN)
  • image generation
  • synthetic aperture radar (SAR)

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